Air pollution leakage tracing method and system based on large airflow field simulation

Abstract

The invention discloses an air pollution leakage tracing method and system based on large airflow field simulation, and belongs to the technical field of air pollution treatment. According to an existing air pollution tracing scheme, pollution source positioning is not accurate enough, the amount of data needing to be processed is very large, and the air pollution leakage early warning efficiency is affected. According to the air pollution leakage traceability method based on large airflow field simulation, a scene data acquisition model, a scene gridding model, a gas diffusion model and a traceability calculation model are constructed, and air pollution reverse calculation is carried out in combination with a computational fluid mechanics simulation algorithm and a source acceptor function algorithm; the pollution source and the monitoring point are associated according to the spatial response relationship between the pollutant calculation data and the pollutant monitoring data, so that the pollution positioning error is small, the accurate pollution source position can be obtained, the data processing amount can be effectively reduced, the simulation cost can be reduced, and the air pollution tracing efficiency can be improved.

Description

Technical Field

[0001] The invention relates to an air pollution leakage tracing method and system based on atmospheric flow field simulation, belonging to the technical field of air pollution treatment. Background Art

[0002] The Chinese patent application (publication number: CN110457829A) provides a source term release inversion and diffusion prediction method based on an integrated atmospheric diffusion model, which includes: Step 1, integrating CFD (Computational Fluid Dynamics), Aermod (Atmospheric Dispersion Modeling System) The first step is to integrate three atmospheric diffusion models, namely, Aermod (System) and Calpuff (non-steady-state Lagrangian puff model system), for simulation calculations at different scales. The second step is to use CFD, Aermod and Calpuff, three atmospheric diffusion models, to build models in advance for the near area of ​​the nuclear power plant, i.e., 5km in the inner area and 10km in the outer area, based on the concept of the plume emergency planning area. The third step is to save the calculation results of various typical wind fields, various accident source items and their release laws, and the concentration variation laws monitored or simulated at characteristic positions under various accident scenarios in the database. The fourth step is to automatically invert the release position and accident type according to the concentration variation law at the characteristic position when a radioactive leakage accident occurs, and quickly calculate the diffusion direction, scope and severity of future pollution based on real-time weather forecast information, so as to assist decision makers in issuing early warnings and formulating emergency response plans.

[0003] The above scheme applies three different meteorological data models to perform simulation calculations at three different spatial scales. The simulation calculation not only calculates the air velocity field, but also needs to use CFD, Aermod, Calpuff and other algorithm models to calculate the changes in pollutant concentrations under various accident source items and their release laws and various accident scenarios. Therefore, the amount of data that needs to be processed is very large, which will affect the efficiency of air pollution leakage warning, and has high requirements on simulation hardware, resulting in high simulation costs, which is not conducive to the promotion and use of air pollution leakage treatment solutions.

[0004] Furthermore, the above scheme calculates the most likely location for pollution emissions through a genetic algorithm. The genetic algorithm requires the changes in pollutant concentrations at the monitoring points calculated forward as the data basis. There will be large errors in pollution positioning, and the accurate location of pollution emissions cannot be obtained. The pollution emission rate and duration of pollution emissions cannot be determined, which affects the efficiency of air pollution treatment. If air pollution leaks cannot be dealt with in a timely manner, it will harm the air quality and ecological environment, and affect the ecosystem and residents' lives.

[0005] The information disclosed in this Background Art is only for understanding the background of the inventive concept and therefore it may include information that does not constitute the prior art. Summary of the invention

[0006] In response to the above problem or one of the above problems, an object of the present invention is to provide an air pollution leakage tracing method and system based on atmospheric flow field simulation, by constructing a scene data acquisition model, a scene gridding model, a gas diffusion model and a tracing calculation model, and combining computational fluid dynamics simulation algorithm and source receiver function algorithm to perform reverse calculation of air pollution, and obtain the pollutant concentration calculation data of a certain monitoring point, and based on the spatial response relationship between the pollutant calculation data and the pollutant monitoring data, the pollution source is associated with the monitoring point to determine the potential pollution source, which can effectively reduce the data processing volume, reduce the simulation cost, and improve the efficiency of air pollution tracing, so that air pollution leaks can be dealt with in a timely manner.

[0007] In response to the above problem or one of the above problems, the second purpose of the present invention is to provide an air pollution leakage tracing method and system based on atmospheric flow field simulation, which adopts computational fluid dynamics method to calculate only the air diffusion velocity field, without calculating the pollutant concentration field. The source of the pollution concentration field is obtained by calculating the source receiver function algorithm, so it can effectively reduce the data processing amount and improve the air pollution leakage warning efficiency, thereby having low requirements for simulation hardware and low simulation cost, and facilitating the promotion and use of air pollution leakage treatment solutions.

[0008] In response to the above problem or one of the above problems, the third object of the present invention is to provide an air pollution leakage tracing method and system based on atmospheric flow field simulation, which performs reverse calculation of air pollution through source-receiver function algorithm, constructs new physical quantities and calculation processes for tracing the source, takes the actual detected pollutant concentration changes as a reference, and uses the physical field calculated by computational fluid dynamics as the basis for simulation. There is no need to forward calculate the pollutant concentration, so the pollution positioning error is small, and not only the pollution leakage location can be determined, but also the pollution emission rate and pollution emission duration can be determined.

[0009] To achieve one of the above purposes, the first technical solution of the present invention is: A method for tracing the source of air pollution leakage based on atmospheric flow field simulation includes the following contents: Through the scenario data collection model created in advance, the geographical data, meteorological data of a certain area and the pollutant monitoring data of a certain monitoring point are obtained; Using the previously created scene grid model, the geographic data of a certain area is processed to construct the geometry and grid simulation objects of the certain area; Using the previously created gas diffusion model, based on the geometry and mesh simulation objects, and through the computational fluid dynamics simulation algorithm, the meteorological data is diffused and the air diffusion flow field is obtained; Based on the previously created source tracing calculation model, according to the air diffusion flow field and pollutant monitoring data, the source-receiver function algorithm is used to perform reverse calculation of air pollution, and the pollutant concentration calculation data of a certain monitoring point is obtained. Based on the spatial response relationship between the pollutant calculation data and the pollutant monitoring data, the pollution source is associated with the monitoring point to determine the potential pollution source.

[0010] The present invention constructs a scene data acquisition model, a scene grid model, a gas diffusion model and a source tracing calculation model, and combines a computational fluid dynamics simulation algorithm and a source receiver function algorithm to perform reverse calculation of air pollution, thereby obtaining the pollutant concentration calculation data of a certain monitoring point, and according to the spatial response relationship between the pollutant calculation data and the pollutant monitoring data, associates the pollution source with the monitoring point, determines the potential pollution source, and effectively improves the efficiency of air pollution tracing, so that air pollution leakage can be dealt with in time, the harm of air pollution to the air quality and the ecological environment can be avoided, and the normal operation of the ecosystem and residents' lives can be ensured. The scheme is scientific, reasonable and feasible.

[0011] Furthermore, the present invention adopts the computational fluid dynamics method to calculate only the air diffusion velocity field, without calculating the pollutant concentration field. The source of the pollution concentration field is calculated by the source-receiver function algorithm. Compared with the existing scheme of calculating the changes in pollutant concentration under various accident source items and their release laws and various accident scenarios, it can effectively reduce the amount of data processing and improve the efficiency of air pollution leakage warning, thereby having low requirements for simulation hardware and low simulation cost, and facilitating the promotion and use of air pollution leakage treatment schemes.

[0012] Furthermore, the present invention performs reverse calculation of air pollution through the source-receiver function algorithm, takes the pollutant concentration as a physical quantity in the calculation domain for reverse calculation, constructs new physical quantities and calculation processes for source tracing calculation, takes the actual detected pollutant concentration changes as a reference, and uses the physical field calculated by computational fluid dynamics as the simulation basis. There is no need to forward calculate the pollutant concentration, so the pollution positioning error is small, and the accurate pollution emission location can be obtained, thereby effectively improving the air pollution treatment efficiency.

[0013] As the preferred technical measures: The method of obtaining geographic data, meteorological data of a certain area and pollutant monitoring data of a certain monitoring point through the scenario data collection model created in advance is as follows: Based on the location coordinates of a certain area to be monitored, obtaining geographic data of a certain area; The geographic data includes building information, topographic information around the building, and altitude; According to the geographical data of a certain area, the meteorological data of a certain area is obtained, which includes wind speed information and wind direction information; At the same time, the monitoring equipment is used to obtain the pollutant monitoring data of a certain monitoring point, which includes the number of pollutant monitoring points, the coordinate position of the pollutant monitoring points, the diffusion wind speed, the diffusion wind direction and the pollutant concentration observation data; The monitoring equipment includes a three-dimensional anemometer and / or an environmental radiation monitor and / or a portable contamination monitor and / or a radioactive radiation detector.

[0014] As the preferred technical measures: The method of using the previously created scene grid model to process the geographic data of a certain area and construct the geometry and grid simulation objects of a certain area is as follows: Obtain geographic data of a certain area, including building information, topographic information around the building, and altitude; Construct a geometric simulation object of a certain area based on building information, topographic information around the building, and altitude; The geometric simulation object is meshed to obtain the geometric and mesh simulation object, which serves as the basis of the computational fluid dynamics simulation algorithm.

[0015] As the preferred technical measures: Using the previously created gas diffusion model, based on the geometry and mesh simulation objects, and through the computational fluid dynamics simulation algorithm, the diffusion simulation of meteorological data is performed to obtain the air diffusion flow field as follows: Obtain meteorological data for a certain area, including wind speed and direction information; According to the wind speed value, set the wind speed range and wind speed reference value; From the wind speed range, take a wind speed value every other wind speed reference value to obtain several wind speed values; According to the wind direction information, the wind direction is divided to obtain multiple wind direction data; Arrange and combine a plurality of wind speed values ​​and a plurality of wind direction data to obtain M groups of wind speed and wind direction combinations; The computational fluid dynamics simulation algorithm is used to perform computational fluid dynamics simulation on M wind speed and wind direction combinations to obtain an air diffusion flow field, which at least includes atmospheric flow field velocity field data.

[0016] As the preferred technical measures: The computational fluid dynamics simulation algorithm is used to simulate the M wind speed and wind direction combinations, and the method for obtaining the air diffusion flow field is as follows: Based on the characteristics of atmospheric flow field, a potential temperature expression is established to measure the heat contained in air blocks at different heights to simulate the flow field in the atmospheric environment; the characteristics of atmospheric flow field include the non-uniformity of temperature, pressure, and air density in the vertical direction, as well as the change information of temperature, pressure, and air density with increasing altitude; According to the potential temperature expression, a control equation group is established, which at least includes the continuity equation, the momentum conservation equation and the energy conservation law; According to the control equations and computational fluid dynamics simulation algorithm, the atmospheric convection diffusion equation is established; Using M groups of wind speed and direction combinations and the turbulent diffusion coefficient, the atmospheric convection-diffusion equation is solved to obtain the air diffusion flow field.

[0017] As the preferred technical measures: Based on the previously created source tracing calculation model, according to the air diffusion flow field and pollutant monitoring data, the source-receiver function algorithm is used to perform reverse calculation of air pollution, and the pollutant concentration calculation data of a certain monitoring point is obtained. Based on the spatial response relationship between the pollutant calculation data and the pollutant monitoring data, the pollution source is associated with the monitoring point. The method for determining the potential pollution source is as follows: Obtain pollutant monitoring data, including the number of pollutant monitoring points, the coordinates of the pollutant monitoring points, the diffusion wind speed, the diffusion wind direction and the pollutant concentration observation data; According to the diffusion wind speed and diffusion wind direction, the corresponding speed data is called from the air diffusion flow field; The corresponding speed data include wind direction data and wind speed data at each monitoring point, which serve as the basis for pollutant source tracing calculations; According to the corresponding velocity data and the atmospheric convection diffusion equation, the convection diffusion equation of pollutants is established; Using the source-receiver function algorithm, based on the pollutant convection-diffusion equation, an adjoint equation is established to perform reverse calculations on the location of a certain monitoring point and obtain the pollutant concentration calculation data of a certain monitoring point; Based on the calculated pollutant concentration data and the observed pollutant concentration data, a cost function is established to quantify the difference between the calculated pollutant concentration distribution and the actually observed pollutant concentration distribution, so as to characterize the spatial response of pollutant concentration to each potential source location; According to the number of pollutant monitoring points and the coordinate positions of the pollutant monitoring points, the cost function is solved and minimized to obtain the optimal source parameter combination, so that the similarity between the calculated results and the observed results is minimized, so as to associate the pollution source with the monitoring point. The source parameter combination includes the source location of the potential pollution source, the emission rate of the pollution source, the emission duration and the emission start time; The optimal source parameter combination includes the optimal potential emission location of the pollution source, the optimal pollution release time, the optimal pollution duration and the optimal pollution emission rate, so as to determine the potential pollution source.

[0018] As the preferred technical measures: Using the source-receiver function algorithm, based on the pollutant convection-diffusion equation, an adjoint equation is established to perform reverse calculations on the location of a certain monitoring point. The method for obtaining the pollutant concentration calculation data of a certain monitoring point is as follows: Based on the pollutant convection-diffusion equation, the source-receiver function is constructed using the source-receiver function algorithm; Using the adjoint equation in the source-receiver function, pre-calculations are performed to characterize the response of the pollutant released at a certain location and time to the monitoring point; Setting a number of source parameter combinations, each source parameter combination includes the source location of the potential pollution source, the pollution source emission rate, the emission duration and the emission start time; According to several source parameter combinations, the adjoint equation is used to reversely calculate the location of a certain monitoring point to obtain the pollutant concentration calculation data of the certain monitoring point.

[0019] As the preferred technical measures: Based on the pollutant concentration calculation data and the pollutant concentration observation data, the method for establishing the cost function is as follows: Calculate the covariance of pollutant concentration calculation data and pollutant concentration observation data to obtain pollutant change covariance, which reflects the degree of common change between the two; Calculate the product of the standard deviation of the pollutant concentration calculation data and the pollutant concentration observation data to obtain the pollutant change product; The pollutant change covariance is used as the numerator and the pollutant change product is used as the denominator to form a cost function, which is used to characterize the similarity between the calculated results and the observed results.

[0020] To achieve one of the above purposes, the second technical solution of the present invention is: A method for tracing the source of air pollution leakage based on atmospheric flow field simulation includes the following contents: Obtain geographical data, meteorological data of a certain area and pollutant monitoring data at a certain monitoring point; Process the geographic data of a certain area and construct the geometric and mesh simulation objects of the area; Based on the geometry and mesh simulation objects, the meteorological data is diffused and simulated by computational fluid dynamics simulation algorithm to obtain the air diffusion flow field, which includes wind direction and wind speed data; Using the source-receiver function, the response of the pollutant concentration measured at a monitoring point to the location of each potential source is calculated, the pollution source is associated with the monitoring point, and the pollution source parameters are optimized to determine the optimal potential emission location, pollution release time, pollution duration and pollution emission rate of the pollution source, thereby realizing air pollution source tracing calculation.

[0021] The present invention utilizes computational fluid dynamics simulation algorithm and source-receiver function algorithm to perform reverse calculation of air pollution, obtains pollutant concentration calculation data of a certain monitoring point, and associates the pollution source with the monitoring point based on the spatial response relationship between the pollutant calculation data and the pollutant monitoring data. It can not only determine the location of pollution leakage, but also determine the pollution emission rate and pollution emission duration. At the same time, the present invention can find the possibility of pollution emission at any point in the calculation domain, rather than just ranking and judging the possibilities of several potential emission sources. Therefore, in the event of an unknown emission source, the pollution can also be traced, effectively improving the efficiency of air pollution tracing and avoiding the harm of air pollution to air quality and the ecological environment.

[0022] To achieve one of the above purposes, the third technical solution of the present invention is: An air pollution leakage tracing system based on atmospheric flow field simulation is used as an optimal technical measure: It includes: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned air pollution leakage tracing method based on atmospheric flow field simulation.

[0023] Compared with the prior art solutions, the present invention has the following beneficial effects: The present invention constructs a scene data acquisition model, a scene grid model, a gas diffusion model and a source tracing calculation model, and combines a computational fluid dynamics simulation algorithm and a source receiver function algorithm to perform reverse calculation of air pollution, thereby obtaining the pollutant concentration calculation data of a certain monitoring point, and according to the spatial response relationship between the pollutant calculation data and the pollutant monitoring data, associates the pollution source with the monitoring point, determines the potential pollution source, and effectively improves the efficiency of air pollution tracing, so that air pollution leakage can be dealt with in time, the harm of air pollution to the air quality and the ecological environment can be avoided, and the normal operation of the ecosystem and residents' lives can be ensured. The scheme is scientific, reasonable and feasible.

[0024] Furthermore, the present invention adopts the computational fluid dynamics method to calculate only the air diffusion velocity field, without calculating the pollutant concentration field. The source of the pollution concentration field is calculated by the source-receiver function algorithm. Compared with the existing scheme of calculating the changes in pollutant concentration under various accident source items and their release laws and various accident scenarios, it can effectively reduce the amount of data processing and improve the efficiency of air pollution leakage warning, thereby having low requirements for simulation hardware and low simulation cost, and facilitating the promotion and use of air pollution leakage treatment schemes.

[0025] Furthermore, the present invention performs reverse calculation of air pollution through the source-receiver function algorithm, takes the pollutant concentration as a physical quantity in the calculation domain for reverse calculation, constructs new physical quantities and calculation processes for source tracing calculation, takes the actual detected pollutant concentration changes as a reference, and uses the physical field calculated by computational fluid dynamics as the simulation basis. There is no need to forward calculate the pollutant concentration, so the pollution positioning error is small, and the accurate pollution emission location can be obtained, thereby effectively improving the air pollution treatment efficiency.

[0026] Furthermore, the present invention utilizes computational fluid dynamics simulation algorithms and source-receiver function algorithms to reversely calculate air pollution, obtains pollutant concentration calculation data of a certain monitoring point, and associates the pollution source with the monitoring point based on the spatial response relationship between the pollutant calculation data and the pollutant monitoring data. This can not only determine the location of pollution leakage, but also the pollution emission rate and pollution emission duration. At the same time, the present invention can find the possibility of pollution emission at any point in the calculation domain, rather than just ranking and judging the possibilities of several potential emission sources. Therefore, in the event of an unknown emission source, the pollution can also be traced, effectively improving the efficiency of air pollution tracing and avoiding the harm of air pollution to air quality and the ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic diagram of a flow chart of the air pollution leakage source tracing method of the present invention; Figure 2 A schematic diagram of a geometric simulation object in an embodiment of the present invention; Figure 3 A schematic diagram of a grid simulation object in an embodiment of the present invention; Figure 4 A schematic diagram of a flow field velocity calculation result of a certain factory area in an embodiment of the present invention; Figure 5 A schematic diagram of the locations of monitoring points and discharge points in an embodiment of the present invention; Figure 6 A schematic diagram of the diffusion of pollutants in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0029] On the contrary, the present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention as defined by the claims. Further, in order to make the public have a better understanding of the present invention, some specific details are described in detail in the following detailed description of the present invention. Those skilled in the art can fully understand the present invention without the description of these details.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention pertains. The terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0031] like Figure 1 As shown, the first specific embodiment of the air pollution leakage tracing method based on atmospheric flow field simulation of the present invention is: A method for tracing the source of air pollution leakage based on atmospheric flow field simulation includes the following contents: Through the scenario data collection model created in advance, the geographical data, meteorological data of a certain area and the pollutant monitoring data of a certain monitoring point are obtained; Using the previously created scene grid model, the geographic data of a certain area is processed to construct the geometry and grid simulation objects of the certain area; Using the previously created gas diffusion model, based on the geometry and mesh simulation objects, and through the computational fluid dynamics simulation algorithm, the meteorological data is diffused and the air diffusion flow field is obtained; Based on the previously created source tracing calculation model, according to the air diffusion flow field and pollutant monitoring data, the source-receiver function algorithm is used to perform reverse calculation of air pollution, and the pollutant concentration calculation data of a certain monitoring point is obtained. Based on the spatial response relationship between the pollutant calculation data and the pollutant monitoring data, the pollution source is associated with the monitoring point to determine the potential pollution source.

[0032] The second specific embodiment of the air pollution leakage source tracing method based on atmospheric flow field simulation of the present invention is: A method for tracing the source of air pollution leakage based on atmospheric flow field simulation can use computational fluid dynamics simulation algorithm and the adjoint probability method to trace the potential pollution sources in a certain area. It includes 5 main steps, as shown below: Step 1. Use the scene grid model to construct the geometry and grid simulation objects of a certain area as the calculation basis of the computational fluid dynamics simulation algorithm; Step 2. Set up multiple pollution monitoring devices and meteorological data monitoring devices in a certain area and its surrounding areas, record meteorological data and changes in pollutant concentrations over time, and monitor the excess of pollutants in a certain area; Step 3. Use computational fluid dynamics simulation algorithm to establish a gas diffusion model and calculate the source-receptor function (SRF) from the potential source point to each monitoring point. Step 4. Use the inverse calculation algorithm to build a source tracing calculation model, and repeatedly optimize the location of the pollution source, release rate and other parameters through the source-receiver function; Step 5. Find the best parameter combination that matches the monitoring data, determine the location of the leak source, the time of the leak, and other information, so as to locate the factory where the waste gas is illegally discharged, trace and stop the environmental pollution in time, and ensure a good and healthy ecological environment.

[0033] A specific embodiment of using the present invention to trace the source of air pollution leakage in a factory: The air pollution leakage source tracing method based on atmospheric flow field simulation is used to trace the pollution leakage of a factory, which includes the following contents: By using the scene gridding model and based on the geometry and grid construction method, the geometry and grid simulation objects that can be used for computational fluid dynamics simulation are obtained; then the scene data acquisition model is used to obtain the meteorological data inside and outside a factory and the data on the change of pollutants over time; then the pollutant diffusion simulation is carried out through the gas diffusion model based on the computational fluid dynamics algorithm; and then according to the tracing calculation model, the source receiver function (SRF) method is used to calculate the response of the pollutant concentration measured at a given monitoring point to the position of each potential source, the pollution source is associated with the monitoring point, and the pollution source parameters are optimized, and finally the optimal potential emission position, pollution release time, pollution duration and pollution emission rate of the pollution source are determined to realize the tracing calculation.

[0034] In this embodiment: the scene grid model, through the terrain data of a factory building and its surroundings, constructs the geometry and grid simulation objects of the factory's atmospheric environment as the basis for computational fluid dynamics simulation calculations. Combined with the geographical location characteristics of a factory, the dominant wind direction and dominant wind speed in this area are determined, a series of atmospheric flow field calculations are performed in advance, flow field velocity data are obtained, and a corresponding atmospheric flow field database is constructed for subsequent calculations.

[0035] In this embodiment: the scene data acquisition model uses three-dimensional anemometer, FD-3013 environmental radiation monitor, XH-3510 portable pollution monitor, OSEN-FS radioactive radiation detector and other equipment to monitor meteorological data and pollutant concentration change data in real time.

[0036] In this embodiment: the gas diffusion model replaces the integral or partial differential equation in the control equation by a discrete algebraic form, and then solves a specific numerical solution for data comparison and explanation of experimental phenomena.

[0037] The governing equations include the continuity equation, the momentum conservation equation, and the energy conservation equation.

[0038] The continuity equation is given by:

[0039] The momentum conservation equation is shown below:

[0040] The energy conservation equation is shown below:

[0041] In the formula is the density, It's time. For speed, For pressure, is the Reynolds number, is the temperature, is the isobaric specific heat capacity, is the heating / cooling source term.

[0042] There are certain differences between the flow behaviors of atmospheric flow fields and ordinary flow fields. Therefore, in order to better simulate atmospheric flow fields, additional formulas are needed to describe the flow fields under atmospheric conditions. The atmospheric flow field is reflected in the vertical direction as the unevenness of temperature, pressure, and air density. As the altitude increases, the temperature, pressure, and air density decrease accordingly. In atmospheric science, the concept of potential temperature is used to measure the amount of heat contained in air parcels at different altitudes. During the dry adiabatic process, although there is no heat balance or latent heat conversion in the air parcel, there will still be obvious temperature changes due to the expansion or compression work during the rise and fall of the air parcel. For different air parcels, air masses with high potential temperatures represent warmer air parcels, and air masses with low potential temperatures represent colder air parcels. Potential temperature The expression is as follows:

[0043]

[0044] In the formula Normal pressure, usually 1000mbar, is the dry air constant.

[0045]

[0046] In the formula is the heating / cooling source term, is the thermal conductivity; is the coordinate, its subscript It represents a certain direction of the coordinate axis. By modifying the relevant formulas, a more realistic simulation calculation of the atmospheric flow field can be achieved.

[0047] At the same time, in order to simulate the diffusion process of atmospheric pollutants, it is also necessary to establish the pollutant convection diffusion equation, and its calculation formula is as follows:

[0048] In the formula, is the pollutant concentration, For time, is the turbulent diffusion coefficient; for speed; is the coordinate, its subscript Indicates a certain direction of the coordinate axis; is the emission rate function; is the Dirac function, The coordinate values ​​representing potential emission points.

[0049] The right side of the equation Indicates that the pollutant is at coordinate At the location of kg / s for discharge, and at a time The emission starts at The emission is stopped at the moment, and the emission time is The expression of the emission rate function is as follows:

[0050] In the above equation, the Dirac function is a piecewise function, which is defined as follows:

[0051] in, is the shear force, is a very small constant value, which is much smaller than the grid space scale of the atmospheric pollution computational fluid dynamics simulation algorithm. This equation is added to set the pollution source as a point. At other locations other than this pollution emission point, the right side of the equation is always 0.

[0052] In this embodiment, a certain factory is set up There are pollution monitoring points (pollution monitoring stations), and the coordinates of the pollution monitoring points are ,in These pollution monitoring points were respectively Record the pollutant concentration at each moment. , Indicates The number of records for each pollution monitoring point. Indicates The pollution monitoring point is Moment The pollutant concentration monitored. The total number of monitoring times at all monitoring points The calculation formula is as follows:

[0053] Then, the problem of estimating source emission rates and source locations can be formulated as finding the source parameters The optimal value of makes the following cost function To reach minimum:

[0054] in, is the number of monitoring stations, Indicates The number of recordings for each monitoring station may be different; Indicates The pollution monitoring point is The pollutant concentration monitored at each moment; For the case where pollutants are emitted at a certain predicted emission point, The pollution monitoring point is The pollutant concentration monitored at each moment; Represents the cost function The minimum value reached.

[0055] In this embodiment: the source tracing calculation model uses the source-receiver function to associate the pollution source with the monitoring point. The source-receiver function is pre-calculated by the adjoint equation. In general transient diffusion problems, the number of adjoint equations that need to be integrated is equal to the number of available measurements. However, in the present invention, it is assumed that the meteorological conditions are stable over a period of time, and the number of integrations of the corresponding adjoint equation can be reduced to the same as the number of measurement stations. The adjoint equation is based on the simulation start time of each measurement station, that is, The instantaneous emission at time is taken as the condition, and the following equations are solved at each measuring station:

[0056] In the formula, Indicates The coordinates of the monitoring stations, is the time variable, Indicated in The velocity component in the direction is calculated by computational fluid dynamics simulation. express Three directions, , represents the Dirac function; is a very small value, which is used to characterize other locations except the monitoring station, and except for the starting time, the source term on the right side of the equation is always 0; Indicates the time when the simulation starts; Indicates The source-receiver function of the monitoring station is function, and It is also a companion variable, indicating that Position in time Response of released pollutants to monitoring points.

[0057] Using the adjoint equation, the pollutant concentration at the monitoring station can be obtained by reverse integration, which is expressed as the integral of the source-receiver function. The specific calculation formula is as follows:

[0058] In the formula, Represents the first The monitoring station is The pollutant concentration monitored at the time, represents the concentration of pollutant released, Indicates the time when pollutant release begins. Indicates the duration of pollutant release, represents the source-sink function, The location where the pollutant is released. Indicates the time when the simulation starts. Indicates The monitoring station is The monitoring time, is the time variable.

[0059] In order to find the most likely pollution emission location, the most likely emission point location is selected by defining a cost function and finding the method that minimizes the cost function. The calculation formula is as follows:

[0060] In the formula, represents the cost function, where is the spatial location parameter, which represents the location of the potential emission point ,in , is the total number of potential emission points; Indicates the possible duration of the emission , Indicates the possible emission start time point , To calculate the concentration, is the observed concentration, is the optimal potential emission location, i.e. the location of the most likely pollution emission point; is the optimal pollution duration parameter, i.e. the most likely emission duration; is the optimal pollution start time parameter, that is, the most likely emission start time.

[0061] In this example, it is assumed that the pollutant release rate is constant during the emission duration, and its upper and lower limits are assumed to be:

[0062] in, is the duration of pollutant release, is the minimum release duration set, is the maximum release duration set. To facilitate program calculation, the release duration is discretized into The possible discrete values ​​of the source durations are:

[0063]

[0064] Furthermore, the start time value The calculation formula is as follows:

[0065] In the formula, is the total computation time.

[0066] Then, the entire calculation time is discretized into the number of minimum release durations, which is calculated as follows:

[0067] Cost Function Used to quantify the difference between the calculated pollutant concentration distribution and the actually observed pollutant concentration distribution. The numerator means the calculated concentration. and observed concentrations The covariance of reflects the degree of common change between the two; the meaning of the denominator is to calculate the concentration and observed concentrations By minimizing the cost function, the optimal source parameter combination can be found to minimize the similarity between the calculated results and the observed results, thereby achieving source inversion, that is, tracing the pollution source. By finding the parameter combination that minimizes the cost function , we can get the location of the most likely pollution emission point as .

[0068] Furthermore, the expression for the most likely emission duration is as follows:

[0069] The expression for the most likely emission start time is as follows:

[0070] A specific embodiment of using the present invention to trace the source of air pollution leakage in a factory area: Taking a factory in a certain place as an example, the air pollution leakage source tracing method based on atmospheric flow field simulation is used to trace the pollution leakage in the factory, which includes the following steps: Step 1: Use the scene grid model and the building and terrain elevation data around a factory area to construct the geometry and mesh simulation objects corresponding to the atmospheric environment around the factory area. The geometry and mesh simulation objects include geometry simulation objects and mesh simulation objects. The geometry simulation objects can be found in Figure 2 , the mesh simulation objects in the local area can be found in Figure 3 .

[0071] Step 2: Obtain the mainstream data (wind speed and wind direction) of the geographical location of a certain factory through the scene data collection model. The main wind speed is ,from The range of every Take a wind speed value, a total of 8 wind speeds, the main wind direction is southeast wind (summer) and northwest wind (winter), take 6 wind directions. Through permutations and combinations, a total of 48 wind speed and wind direction combinations are obtained, and the gas diffusion model is used to perform computational fluid dynamics simulation. The calculated atmospheric flow field velocity data is saved in the corresponding database and called when running the traceability calculation. The flow field velocity distribution of a certain factory area can be seen in Figure 4 , the wind speed range is 0.0-0.7 In the figure, the more red the area is, the stronger the wind speed is, and the more blue the area is, the slower the wind speed is.

[0072] Step 3: The monitoring station monitors wind speed, wind direction, and pollutant concentration in real time. Once it detects that the pollutant concentration exceeds the standard, the subsequent pollutant source tracing calculation program is initiated.

[0073] Step 4: Read the monitoring station data to obtain wind speed, wind direction, and pollutant concentration data. By monitoring the wind speed and wind direction, call the corresponding speed data from the atmospheric flow velocity database to determine the wind direction and wind speed data at each monitoring station location as the basis for pollutant source tracing calculations.

[0074] Step 5: Use the source tracing calculation model to construct the source-receiver function and cost function of atmospheric pollutants.

[0075] The source-sink functions are:

[0076] In the formula, Indicates The coordinates of the monitoring stations, is the time variable, Indicated in The velocity component in the direction is calculated by computational fluid dynamics simulation. express Three directions, , represents the Dirac function, is a very small value, used to characterize locations other than monitoring stations; Indicates the time when the simulation starts. Indicates The source-receiver function of the monitoring station is function, and it is also the accompanying variable, indicating that Position in time Response of released pollutants to monitoring points.

[0077] The cost function is:

[0078] In the formula, represents the cost function, where Indicates the location of potential emission points ,in , is the total number of potential emission points, Indicates the possible duration of the emission , Indicates the possible emission start time point , Indicated in The corresponding spatial position, The corresponding emission duration, The corresponding emission start time point.

[0079] In order to facilitate the writing of the inversion calculation program and use of computers for calculations, a fixed minimum pollutant emission time interval is set, and the total calculation time is set to an integer multiple of the minimum pollutant emission time interval.

[0080] The emission duration sets its upper and lower limits to:

[0081] in, is the duration of pollutant release, is the minimum release duration set, is the maximum release duration set. To facilitate program calculation, the release duration is discretized into The possible discrete values ​​of the source durations are:

[0082]

[0083] Then, based on the cost function, the start time value is calculated , and its calculation formula is as follows:

[0084] In the formula, is the total computation time, , is the number of minimum release durations that discretize the entire computation time.

[0085] In this embodiment, the location of the pollution monitoring point O and the potential emission point is as follows: Figure 5 As shown, there are 21 pollution monitoring stations and 7 potential emission points. . Figure 5 The stars in the figure indicate potential emission sites, which are numbered S1-S7; Figure 5 The red circles in the figure represent the pollution monitoring points, which are numbered O1-O21. The minimum release duration is set to 10 seconds and the maximum release duration is set to 1 minute. , , The calculation start time refers to the average wind speed data of 2.5m / s and the scope of a certain factory area. It takes at least 400 seconds for pollution to reach the monitoring station, so the calculation time is set to 600 seconds. , .

[0086] Step 6: Traverse all parameters, including spatial position parameters , pollution duration parameter , pollution start time parameter , find the cost function The minimum corresponding parameter , , Through pollution source tracing calculation, the location of the most likely pollution emission point is obtained as , the emission duration is The emission start time is .

[0087] In this embodiment, for the purpose of testing, it is assumed that there is a pollutant emission phenomenon at the emission point S1, and the emission lasts for 20 seconds. The pollutant diffusion process and data are obtained by computational fluid dynamics simulation calculation, such as Figure 6 As shown, the pollutant concentration range is 0.000-0.007 In the figure, the more red the area is, the greater the pollutant concentration in the area is, and the more blue the area is, the smaller the pollutant concentration in the area is. Based on this, the test is carried out. Through calculation, the cost function of the potential emission point S1 is 0.0007, which is the lowest among the 7 emission points, and therefore it is also the location most likely to cause pollution.

[0088] An embodiment of a device applying the method of the present invention: An electronic device comprising: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned air pollution leakage tracing method based on atmospheric flow field simulation.

[0089] A computer medium embodiment using the method of the present invention: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned air pollution leakage tracing method based on atmospheric flow field simulation.

[0090] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, and computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program codes.

[0091] The present application is described by flowcharts or / and block diagrams of the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each process or / and block in the flowchart or / and block diagram and the combination of the processes or / and blocks in the flowchart or / and block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart or / and block diagram. Figure 1 Process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0092] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 Process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 Process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0094] The model in this application is an object that objectively describes the morphological structure with the help of physical or virtual representation. The object is not equal to the object and is not limited to physical and virtual. It can be a data processing function, software program, processing mode, usage method, operation method, workflow, application process, electronic hardware, circuit module, processing system, system imitation or simulation object.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field can still modify or replace the specific implementation methods of the present invention with equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A method for tracing the source of air pollution leakage based on atmospheric flow field simulation, characterized in that: Includes the following: Through the scenario data collection model created in advance, the geographical data, meteorological data of a certain area and the pollutant monitoring data of a certain monitoring point are obtained; Using the previously created scene grid model, the geographic data of a certain area is processed to construct the geometry and grid simulation objects of the certain area; Using the previously created gas diffusion model, based on the geometry and mesh simulation objects, and through the computational fluid dynamics simulation algorithm, the meteorological data is diffused and the air diffusion flow field is obtained; Based on the previously created source tracing calculation model, according to the air diffusion flow field and pollutant monitoring data, the source-receiver function algorithm is used to perform reverse calculation of air pollution, and the pollutant concentration calculation data of a certain monitoring point is obtained. Based on the spatial response relationship between the pollutant calculation data and the pollutant monitoring data, the pollution source is associated with the monitoring point to determine the potential pollution source.

2. The air pollution leakage tracing method based on atmospheric flow field simulation according to claim 1 is characterized by: The method of obtaining geographic data, meteorological data of a certain area and pollutant monitoring data of a certain monitoring point through the scenario data collection model created in advance is as follows: Based on the location coordinates of a certain area to be monitored, obtaining geographic data of a certain area; The geographic data includes building information, topographic information around the building, and altitude; According to the geographical data of a certain area, the meteorological data of a certain area is obtained, which includes wind speed information and wind direction information; At the same time, the monitoring equipment is used to obtain the pollutant monitoring data of a certain monitoring point, which includes the number of pollutant monitoring points, the coordinate position of the pollutant monitoring points, the diffusion wind speed, the diffusion wind direction and the pollutant concentration observation data; The monitoring equipment includes a three-dimensional anemometer and / or an environmental radiation monitor and / or a portable contamination monitor and / or a radioactive radiation detector.

3. The method for tracing the source of air pollution leakage based on atmospheric flow field simulation according to claim 2, characterized in that: The method of using the previously created scene grid model to process the geographic data of a certain area and construct the geometry and grid simulation objects of a certain area is as follows: Obtain geographic data of a certain area, including building information, topographic information around the building, and altitude; Construct a geometric simulation object of a certain area based on building information, topographic information around the building, and altitude; The geometric simulation object is meshed to obtain the geometric and mesh simulation object, which serves as the basis of the computational fluid dynamics simulation algorithm.

4. The method for tracing the source of air pollution leakage based on atmospheric flow field simulation according to claim 1, characterized in that: Using the previously created gas diffusion model, based on the geometry and mesh simulation objects, and through the computational fluid dynamics simulation algorithm, the diffusion simulation of meteorological data is performed to obtain the air diffusion flow field as follows: Obtain meteorological data for a certain area, including wind speed and direction information; According to the wind speed value, set the wind speed range and wind speed reference value; From the wind speed range, take a wind speed value every other wind speed reference value to obtain several wind speed values; According to the wind direction information, the wind direction is divided to obtain multiple wind direction data; Arrange and combine a plurality of wind speed values ​​and a plurality of wind direction data to obtain M groups of wind speed and wind direction combinations; The computational fluid dynamics simulation algorithm is used to perform computational fluid dynamics simulation on M wind speed and wind direction combinations to obtain an air diffusion flow field, which at least includes atmospheric flow field velocity field data.

5. The method for tracing the source of air pollution leakage based on atmospheric flow field simulation according to claim 4, characterized in that: The computational fluid dynamics simulation algorithm is used to simulate the M wind speed and wind direction combinations, and the method for obtaining the air diffusion flow field is as follows: Based on the characteristics of atmospheric flow field, a potential temperature expression is established to measure the heat contained in air blocks at different heights to simulate the flow field in the atmospheric environment; the characteristics of atmospheric flow field include the non-uniformity of temperature, pressure, and air density in the vertical direction, as well as the change information of temperature, pressure, and air density with increasing altitude; According to the potential temperature expression, a control equation group is established, which at least includes the continuity equation, the momentum conservation equation and the energy conservation law; According to the control equations and computational fluid dynamics simulation algorithm, the atmospheric convection diffusion equation is established; Using M groups of wind speed and direction combinations and the turbulent diffusion coefficient, the atmospheric convection-diffusion equation is solved to obtain the air diffusion flow field.

6. The method for tracing the source of air pollution leakage based on atmospheric flow field simulation according to claim 5, characterized in that: Based on the previously created source tracing calculation model, according to the air diffusion flow field and pollutant monitoring data, the source-receiver function algorithm is used to perform reverse calculation of air pollution, and the pollutant concentration calculation data of a certain monitoring point is obtained. Based on the spatial response relationship between the pollutant calculation data and the pollutant monitoring data, the pollution source is associated with the monitoring point. The method for determining the potential pollution source is as follows: Obtain pollutant monitoring data, including the number of pollutant monitoring points, the coordinates of the pollutant monitoring points, the diffusion wind speed, the diffusion wind direction and the pollutant concentration observation data; According to the diffusion wind speed and diffusion wind direction, the corresponding speed data is called from the air diffusion flow field; The corresponding speed data include wind direction data and wind speed data at each monitoring point, which serve as the basis for pollutant source tracing calculations; According to the corresponding velocity data and the atmospheric convection diffusion equation, the convection diffusion equation of pollutants is established; Using the source-receiver function algorithm, based on the pollutant convection-diffusion equation, an adjoint equation is established to perform reverse calculations on the location of a certain monitoring point and obtain the pollutant concentration calculation data of a certain monitoring point; Based on the calculated pollutant concentration data and the observed pollutant concentration data, a cost function is established to quantify the difference between the calculated pollutant concentration distribution and the actually observed pollutant concentration distribution, so as to characterize the spatial response of pollutant concentration to each potential source location; According to the number of pollutant monitoring points and the coordinate positions of the pollutant monitoring points, the cost function is solved and minimized to obtain the optimal source parameter combination, so that the similarity between the calculated results and the observed results is minimized, so as to associate the pollution source with the monitoring point. The source parameter combination includes the source location of the potential pollution source, the emission rate of the pollution source, the emission duration and the emission start time; The optimal source parameter combination includes the optimal potential emission location of the pollution source, the optimal pollution release time, the optimal pollution duration and the optimal pollution emission rate, thereby determining the pollution source.

7. The method for tracing the source of air pollution leakage based on atmospheric flow field simulation according to claim 6, characterized in that: Using the source-receiver function algorithm, based on the pollutant convection-diffusion equation, an adjoint equation is established to perform reverse calculations on the location of a certain monitoring point. The method for obtaining the pollutant concentration calculation data of a certain monitoring point is as follows: Based on the pollutant convection-diffusion equation, the source-receiver function is constructed using the source-receiver function algorithm; Using the adjoint equation in the source-receiver function, pre-calculations are performed to characterize the response of the pollutant released at a certain location and time to the monitoring point; Setting a number of source parameter combinations, each source parameter combination includes the source location of the potential pollution source, the pollution source emission rate, the emission duration and the emission start time; According to several source parameter combinations, the adjoint equation is used to reversely calculate the location of a certain monitoring point to obtain the pollutant concentration calculation data of the certain monitoring point.

8. The method for tracing the source of air pollution leakage based on atmospheric flow field simulation according to claim 6, characterized in that: Based on the pollutant concentration calculation data and the pollutant concentration observation data, the method for establishing the cost function is as follows: Calculate the covariance of pollutant concentration calculation data and pollutant concentration observation data to obtain pollutant change covariance, which reflects the degree of common change between the two; Calculate the product of the standard deviation of the pollutant concentration calculation data and the pollutant concentration observation data to obtain the pollutant change product; The pollutant change covariance is used as the numerator and the pollutant change product is used as the denominator to form a cost function, which is used to characterize the similarity between the calculated results and the observed results.

9. A method for tracing the source of air pollution leakage based on atmospheric flow field simulation, characterized in that: Includes the following: Obtain geographical data, meteorological data of a certain area and pollutant monitoring data at a certain monitoring point; Process the geographic data of a certain area and construct the geometric and mesh simulation objects of the area; Based on the geometry and mesh simulation objects, the meteorological data is diffused and simulated by computational fluid dynamics simulation algorithm to obtain the air diffusion flow field, which includes wind direction and wind speed data; Using the source-receiver function, the response of the pollutant concentration measured at a monitoring point to the location of each potential source is calculated, the pollution source is associated with the monitoring point, and the pollution source parameters are optimized to determine the optimal potential emission location, pollution release time, pollution duration and pollution emission rate of the pollution source, thereby realizing air pollution source tracing calculation.

10. An air pollution leakage tracing system based on atmospheric flow field simulation, characterized in that: It includes: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement an air pollution leakage tracing method based on atmospheric flow field simulation as described in any one of claims 1-9.

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